The comparative impact of lexical translation and lexical inferencing on EFL learners’ vocabulary retention
Bibliographic record
Abstract
The present study is an attempt to investigate the comparative effects of lexical translation and lexical inferencing techniques on Female intermediate EFL learners' vocabulary retention.For this purpose, 90 female learners attending the Jahad Daneshgahi Center in Qom took a piloted sample KET test, 60 of whom were selected as homogenous learners.They were randomly divided into two experimental groups-one learning new vocabulary items through lexical translation technique and the other with the lexical inferencing technique.They were given a pre-test on vocabulary to ensure that the participants had no prior knowledge of the target words.Then all participants in both groups were taught using the same material and received the same amount of instruction.The only difference was for teaching of new lexical items.One experimental group was taught mainly through the lexical translation technique while the other experimental group learned by the lexical inferencing technique.After conducting the treatment, a post-test was administered to both groups in order to measure the students' ability in the retention of the lexical items taught through lexical translation and lexical inferencing techniques after a two-week interval.The analysis of the test scores using independent sample t-test revealed that the lexical inferencing group significantly outperformed the lexical translation group on the retention of the lexical items suggesting its benefits for teaching new words.Findings provide insights to teachers as well as students on how to best approach learning new lexical items.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".